feather river redd data exploration
2024-09-26
Goal of this document is to review the Feather River redd data and understand the following:
- what data are collected?
- what is the quality of the data?
- what are the data limitations?
- how are these data useful to Science Plan hypotheses?
Relevant Science Plan hypotheses
- Spawning habitat acreage
From Science Plan: “The metric for this hypothesis will be the acreage of spawning habitat with suitable water depths and velocities, and sizes of spawning gravel. Spawning habitat criteria, including depth, velocity, and target spawning substrate size will be defined in the specific VA action science and monitoring plan and associated design documents. The suitable gravel size for spawning habitat will be a range and distribution of spawning substrate sizes specific to the spawning population and hydrogeomorphic conditions in each tributary.”
Ideas: This hypothesis was set up to use depth and velocity criteria to map spawning habitat acreage and compare to existing habitat. We could map spawning habitat based on redd locations over time. We would need latitude and longitude of all redds observed.
- Salmon redd density (this is the most relevant hypothesis)
From Science Plan: “The metric for this hypothesis will be the number of Chinook salmon redds per unit area in habitat enhancement project areas, while also accounting for the potential for redd superimposition. The baseline for this hypothesis will be the redd density and superimposition rate at habitat enhancement locations compared to adjacent areas within the same reach, measured concurrently along with water quality criteria. In systems where redd mapping has been conducted consistently at both project locations and adjacent, non-enhanced locations, historical data can also be leveraged to examine trends and changes in redd density after the enhancement action.”
Ideas: This is the number of redds per unit area. We would need a count of redds and some sort of delineation of the area where redds are occurring (this could be latitude/longitude of redd or river mile/reach surveyed, assuming whole area is surveyed)
- Natural origin adult Chinook salmon population estimates by tributary, and trend in abundance (harvest plus escapement)
From Science Plan: “The metric for this hypothesis will be annual natural-origin adult Chinook salmon cohort replacement rates and trends over multiple years (e.g., > 3 years) over the period of VA implementation. The baseline for this hypothesis will be the annual natural-origin adult Chinook salmon cohort replacement rate trends during the period associated with the Anadromous Fish Restoration Program Doubling Goal (years 1967-1991). A secondary baseline, to reflect recent conditions and population numbers, will be annual adult Chinook salmon cohort replacement rates and trends for natural-origin fall run Chinook salmon since 2010.”
Ideas: We would need high quality redd data in order to estimate adult population
Contact
These data were originally acquired from Chris Cook
Timeframe
- 2014-2023
Data quality
Data questions
- Latitude/Longitude unit system is inconsistent. Only 2023 has UTM coordinate system, other years following are shown as “latitude mn / longitude me” (probably zone 10). This is some information we have found about that coordinate system. Why were the coordinate systems different?
- 2023 data has 6 fields for types of soil (boulder, cobble, pebble,
gravel, sand and silt), other years have 5 (fines, small, med, large).
They were matched by size but % silt was left out for now. Should we
include that column in combined data?
- 2014-10-20 has 6 records with high number_salmon numbers (over 15, check if these are not errors)
- Velocity on 2020-11–18 is negative, is it safe to assume this is a mistake, we turn these into NA
- There are some pretty high pot_depth_m over 40, is it safe to assume this is a mistake, we turn these into NA
- There are five record values for number of salmon that are over 20 (2014-10-20), can we confirm these are accurate?
Combining all years into one object
## Rows: 36,876
## Columns: 22
## $ date <date> 2014-09-09, 2014-09-09, 2014-09-15, 2014-09-15, 2014-…
## $ survey_wk <chr> "1-1", "1-1", "2-1", "2-1", "2-1", "2-1", "2-1", "2-1"…
## $ location <chr> "moe's side channel", "upper cottonwood", "cottonwood"…
## $ file_number <dbl> 1, 2, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 1…
## $ type <chr> "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p",…
## $ number_redds <dbl> 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ number_salmon <dbl> 1, 1, 1, 0, 3, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, …
## $ depth_m <dbl> 0.30, 0.62, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.72, …
## $ pot_depth_m <dbl> 0.50, 0.70, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.76, …
## $ velocity_m_s <dbl> 0.61, 0.89, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.55, …
## $ percent_fines <dbl> 40, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 0, 0, 0,…
## $ percent_small <dbl> 20, 65, NA, NA, NA, NA, NA, NA, NA, NA, NA, 40, 40, 60…
## $ percent_med <dbl> 35, 30, NA, NA, NA, NA, NA, NA, NA, NA, NA, 50, 60, 20…
## $ percent_large <dbl> 5, 5, NA, NA, NA, NA, NA, NA, NA, NA, NA, 10, 0, 10, 0…
## $ percent_boulder <dbl> 0, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 0, 10, 0,…
## $ redd_width_m <dbl> 1.1, 2.5, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1.5, 2.0…
## $ redd_length_m <dbl> 2.0, 3.5, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2.0, 2.5…
## $ longitude <dbl> 0.0000, -121.5542, -121.5558, -121.5557, -121.5556, -1…
## $ latitude <dbl> 0.00000, 39.51675, 39.51665, 39.51663, 39.51666, 39.51…
## $ elevation_ft <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ accuracy_ft <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ boat_point <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…Data exploration
Exploring fields that have data consistently
date
- Surveys are conducted September to December
## [1] "2014-09-09" "2023-11-29"Looking at temporal coverage
Since there are outliers, will remove and plot again
location
- Location clean up is needed. There are some names that appear less than 5 times. We could check if they correspond to another location name, and it is spelled differently
##
## aleck aleck riffle
## 90 32
## bedrock bedrock riffle
## 898 1982
## below big hole east below lower auditorium
## 59 481
## big bar big bar riffle
## 33 24
## big hole east big hole west
## 274 47
## big riffle cottonwood
## 104 1214
## developing developing riffle
## 4 3
## eye eye side channel
## 42 107
## g-95 east side channel bottom g-95 east top
## 1 1
## g-95 main g-95 west side channel bottom
## 1 1
## g95 east bottom g95 east side channel bottom
## 137 66
## g95 east side channel top g95 east top
## 34 103
## g95 main g95 west
## 67 40
## g95 west bottom g95 west side channel
## 1 23
## gateway gateway main channel
## 42 15
## gateway side channel goose
## 8 22
## goose riffle great western
## 7 7
## hatchery hatchery pipe
## 1594 892
## hatchery riffle hatchery side channel
## 2169 136
## hatchery side channel upper high flow
## 1 1
## hour keister
## 35 105
## lower auditorium lower big riffle
## 6052 8
## lower hour lower mcfarland
## 225 31
## lower robinson lower table mountain
## 998 912
## lower vance east mathews
## 35 190
## matthews mid auditorium
## 75 102
## mid hour middle auditorium
## 12 1483
## moe's ditch moe's side channel
## 65 2608
## palm palm riffle
## 4 1
## steep steep riffle
## 92 146
## steep side steep side channel
## 54 98
## table mountain top of auditorium
## 1050 3588
## top of audtorium top of hatchery
## 318 9
## trailer park upper auditorium
## 1607 2313
## upper big hole east upper cottonwood
## 2 644
## upper hatchery upper hatchery riffle
## 696 909
## upper hour upper hour east
## 99 2
## upper mathews upper matthews
## 134 33
## upper mcfarland upper robinson
## 38 843
## vance east vance west
## 169 50
## weir weir riffle
## 105 168## [1] "moe's side channel" "upper cottonwood"
## [3] "cottonwood" "lower auditorium"
## [5] "top of auditorium" "upper hatchery riffle"
## [7] "upper auditorium" "mid auditorium"
## [9] "hatchery riffle" "developing riffle"
## [11] "upper mcfarland" "big riffle"
## [13] "g95 east bottom" "g95 main"
## [15] "big hole east" "vance east"
## [17] "below big hole east" "upper hour east"
## [19] "upper hour" "mid hour"
## [21] "keister" "big bar"
## [23] "lower mcfarland" "middle auditorium"
## [25] "upper hatchery" "hatchery"
## [27] "hatchery side channel" "table mountain"
## [29] "goose" "g95 west side channel"
## [31] "upper big hole east" "g95 east side channel bottom"
## [33] "g-95 east top" "g-95 main"
## [35] "lower hour" "hour"
## [37] "g95 east side channel top" "developing"
## [39] "moe's ditch" "lower robinson"
## [41] "trailer park" "mathews"
## [43] "g-95 east side channel bottom" "g-95 west side channel bottom"
## [45] "upper robinson" "weir riffle"
## [47] "steep riffle" "eye side channel"
## [49] "steep side channel" "aleck"
## [51] "lower vance east" "vance west"
## [53] "g95 east top" "upper mathews"
## [55] "great western" "lower table mountain"
## [57] "top of hatchery" "bedrock"
## [59] "hatchery pipe" "steep"
## [61] "below lower auditorium" "big hole west"
## [63] "lower big riffle" "weir"
## [65] "eye" "gateway"
## [67] "g95 west" "palm"
## [69] NA "matthews"
## [71] "high flow" "upper matthews"
## [73] "bedrock riffle" "aleck riffle"
## [75] "gateway side channel" "gateway main channel"
## [77] "goose riffle" "palm riffle"
## [79] "g95 west bottom" "top of audtorium"
## [81] "hatchery side channel upper" "big bar riffle"
## [83] "steep side"Locations surveyed less than 5 times
For locations that are being surveyed less than five times, we might want to verify if they belong to other location area names. Otherwise, these sites might not have enough data to reliably analyze over time.
Frequent locations and years surveyed
type
Observations by type and date
## [1] "p" "a" NA
Count of observations by type
- note that all 2023 data does not have a
typefield - based on conversations with the Feather River team we should remove the “a” observations and this field
redd_count
Redd counts by year
## [1] 0 15## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.000 1.000 1.002 1.000 15.000
Same plot than above but with date values aggregated - using geom_col()
salmon_count
Salmon counts by year
- Noticed some outliers. Plotting below again removing values over 20
## [1] 0 50## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 0.000 0.000 0.297 0.000 50.000 6327
depth
## [1] 0.0 1.5## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 0.32 0.44 0.45 0.56 1.50 31807
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 0.32 0.44 0.45 0.56 1.50 31807velocity
## [1] -0.013 2.740## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -0.01 0.33 0.48 0.52 0.66 2.74 32903Turning that negative velocity to NA and plotting
pot_depth_m
## [1] NA NA## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 0.42 0.51 0.55 0.63 50.00 32197
Same plot as above but outliers removed
Turning values over 40 to NA
latitude/ longitude plot
Cleaning lat/long to plot on a map
summary of depth
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 0.32 0.44 0.45 0.56 1.50 31807summary of pot_depth after modifications
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 0.42 0.51 0.53 0.63 1.70 32199summary of velocity_m_s after modifications
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 0.33 0.48 0.52 0.66 2.74 32904